Source code for pytorch_lightning.strategies.tpu_spawn
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import os
from typing import Any, Dict, List, Optional, Union
import torch
from torch.nn import Module
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning.overrides import LightningDistributedModule
from pytorch_lightning.plugins.io.xla_plugin import XLACheckpointIO
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.ddp_spawn import DDPSpawnStrategy
from pytorch_lightning.strategies.launchers.xla_spawn import _XLASpawnLauncher
from pytorch_lightning.trainer.connectors.data_connector import DataConnector
from pytorch_lightning.trainer.states import TrainerFn
from pytorch_lightning.utilities import _TPU_AVAILABLE, find_shared_parameters, set_shared_parameters
from pytorch_lightning.utilities.data import has_len
from pytorch_lightning.utilities.distributed import ReduceOp
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.optimizer import optimizers_to_device
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from pytorch_lightning.utilities.seed import reset_seed
from pytorch_lightning.utilities.types import _PATH, STEP_OUTPUT
if _TPU_AVAILABLE:
import torch_xla.core.xla_env_vars as xenv
import torch_xla.core.xla_model as xm
import torch_xla.distributed.xla_multiprocessing as xmp
from torch_xla.core.xla_model import rendezvous
from torch_xla.distributed.parallel_loader import MpDeviceLoader
else:
xm, xmp, MpDeviceLoader, rendezvous = [None] * 4
[docs]class TPUSpawnStrategy(DDPSpawnStrategy):
"""Strategy for training multiple TPU devices using the :func:`torch_xla.distributed.xla_multiprocessing.spawn`
method."""
strategy_name = "tpu_spawn"
def __init__(
self,
accelerator: Optional["pl.accelerators.accelerator.Accelerator"] = None,
parallel_devices: Optional[List[int]] = None,
checkpoint_io: Optional[XLACheckpointIO] = None,
precision_plugin: Optional[PrecisionPlugin] = None,
debug: bool = False,
**_: Any,
) -> None:
checkpoint_io = checkpoint_io or XLACheckpointIO()
super().__init__(
accelerator=accelerator,
parallel_devices=parallel_devices,
checkpoint_io=checkpoint_io,
precision_plugin=precision_plugin,
)
self.debug = debug
self.tpu_local_core_rank = 0
self.tpu_global_core_rank = 0
self.start_method = "fork"
@property
def global_rank(self) -> int:
return self.tpu_global_core_rank
@property
def local_rank(self) -> int:
return self.tpu_local_core_rank
@property
def world_size(self) -> int:
return xm.xrt_world_size()
@property
def root_device(self) -> torch.device:
return xm.xla_device()
@staticmethod
def _validate_dataloader(dataloaders: Union[List[DataLoader], DataLoader]) -> None:
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
if not has_len(dataloader):
raise MisconfigurationException(
"TPUs do not currently support IterableDataset objects, the dataset must implement `__len__`."
" HINT: You can mock the length on your dataset to bypass this MisconfigurationException."
)
@staticmethod
def _validate_patched_dataloaders(model: "pl.LightningModule") -> None:
"""Validate and fail fast if the dataloaders were passed directly to fit."""
connector: DataConnector = model.trainer._data_connector
sources = (
connector._train_dataloader_source,
connector._val_dataloader_source,
connector._test_dataloader_source,
connector._predict_dataloader_source,
)
for source in sources:
if not source.is_module():
TPUSpawnStrategy._validate_dataloader(source.instance)
[docs] def connect(self, model: "pl.LightningModule") -> None:
TPUSpawnStrategy._validate_patched_dataloaders(model)
self.wrapped_model = xmp.MpModelWrapper(LightningDistributedModule(model))
return super().connect(model)
def _configure_launcher(self):
self._launcher = _XLASpawnLauncher(self)
[docs] def setup(self, trainer: "pl.Trainer") -> None:
self.start_method = "fork"
self.accelerator.setup(trainer)
if self.debug:
os.environ["PT_XLA_DEBUG"] = str(1)
shared_params = find_shared_parameters(self.model)
self.model_to_device()
if is_overridden("on_post_move_to_device", self.lightning_module):
self.model.module.on_post_move_to_device()
else:
set_shared_parameters(self.model.module, shared_params)
self.setup_precision_plugin()
if trainer.state.fn == TrainerFn.FITTING:
self.setup_optimizers(trainer)
optimizers_to_device(self.optimizers, self.root_device)
def _setup_model(self, model: Module) -> Module:
return model
@property
def distributed_sampler_kwargs(self) -> Dict[str, int]:
return dict(num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())
@property
def is_distributed(self) -> bool:
# HOST_WORLD_SIZE is None outside the xmp.spawn process
return os.getenv(xenv.HOST_WORLD_SIZE, None) and self.world_size != 1
[docs] def process_dataloader(self, dataloader: DataLoader) -> MpDeviceLoader:
TPUSpawnStrategy._validate_dataloader(dataloader)
dataloader = MpDeviceLoader(dataloader, self.root_device)
# Mimic interface to torch.utils.data.DataLoader
dataloader.dataset = dataloader._loader.dataset
return dataloader
def configure_ddp(self) -> None:
pass
def init_dist_connection(self, global_rank: int, world_size: int) -> None:
pass
def set_world_ranks(self, process_idx: int = 0) -> None:
pass
[docs] def barrier(self, name: Optional[str] = None) -> None:
if self.is_distributed:
rendezvous(name)
[docs] def broadcast(self, obj: object, src: int = 0) -> object:
if not self.is_distributed:
return obj
buffer = io.BytesIO()
torch.save(obj, buffer)
data = bytearray(buffer.getbuffer())
data_tensor = torch.tensor(data, device=self.root_device, dtype=torch.float)
data = xm.all_gather(data_tensor)
buffer = io.BytesIO(data.cpu().byte().numpy())
obj = torch.load(buffer)
return obj
[docs] def reduce_boolean_decision(self, decision: bool) -> bool:
decision = torch.tensor(int(decision), device=self.root_device)
decision = self.reduce(decision, reduce_op="sum")
decision = bool(decision == self.world_size)
return decision
[docs] def reduce(self, output, group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = None):
if not isinstance(output, torch.Tensor):
output = torch.tensor(output, device=self.root_device)
_invalid_reduce_op = isinstance(reduce_op, ReduceOp) and reduce_op != ReduceOp.SUM
_invalid_reduce_op_str = isinstance(reduce_op, str) and reduce_op.lower() not in ("sum", "mean", "avg")
if _invalid_reduce_op or _invalid_reduce_op_str:
raise MisconfigurationException(
"Currently, TPUSpawn Strategy only support `sum`, `mean`, `avg` reduce operation."
)
output = xm.mesh_reduce("reduce", output, sum)
if isinstance(reduce_op, str) and reduce_op.lower() in ("avg", "mean"):
output = output / self.world_size
return output
def _worker_setup(self, process_idx: int):
reset_seed()
self.tpu_local_core_rank = xm.get_local_ordinal()
self.tpu_global_core_rank = xm.get_ordinal()
rank_zero_only.rank = self.global_rank
[docs] def validation_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
with self.precision_plugin.val_step_context():
return self.model(*args, **kwargs)
[docs] def test_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
with self.precision_plugin.test_step_context():
return self.model(*args, **kwargs)
[docs] def predict_step(self, *args, **kwargs) -> STEP_OUTPUT:
with self.precision_plugin.predict_step_context():
return self.model(*args, **kwargs)
def training_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:
self._pod_progress_bar_force_stdout()
return output
def validation_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:
self._pod_progress_bar_force_stdout()
return output
def test_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:
self._pod_progress_bar_force_stdout()
return output
def _pod_progress_bar_force_stdout(self) -> None:
# Why is it required? The way `pytorch_xla.distributed` streams logs
# from different vms to the main worker doesn't work well with tqdm
# Ref: https://github.com/pytorch/xla/blob/master/torch_xla/distributed/xla_dist.py#L140
# The print statement seems to force tqdm to flush stdout.
if self.tpu_global_core_rank == 0 and int(os.getenv(xenv.TPUVM_MODE, 0)) == 1:
print()
[docs] def save_checkpoint(
self, checkpoint: Dict[str, Any], filepath: _PATH, storage_options: Optional[Any] = None
) -> None:
"""Save model/training states as a checkpoint file through state-dump and file-write.
Args:
checkpoint: dict containing model and trainer state
filepath: write-target file's path
storage_options: parameter for how to save to storage, passed to ``CheckpointIO`` plugin
"""
# `xla_model.save` needs to be called on all ranks. It internally checks if the local rank is 0
self.checkpoint_io.save_checkpoint(checkpoint, filepath, storage_options=storage_options)
[docs] def remove_checkpoint(self, filepath: _PATH) -> None:
"""Remove checkpoint filepath from the filesystem.
Args:
filepath: Path to checkpoint
"""
if self.local_rank == 0:
self.checkpoint_io.remove_checkpoint(filepath)
[docs] def all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> torch.Tensor:
"""
Function to gather a tensor from several distributed processes
Args:
tensor: tensor of shape (batch, ...)
group: not available with TPUs
sync_grads: not available with TPUs
Return:
A tensor of shape (world_size, batch, ...)
"""
if isinstance(tensor, torch.Tensor) and tensor.dim() == 0:
tensor = tensor.unsqueeze(0)
return xm.all_gather(tensor)
@classmethod
def register_strategies(cls, strategy_registry: Dict) -> None:
strategy_registry.register(
"tpu_spawn_debug", cls, description="TPUSpawn Strategy with `debug` as True", debug=True
)
strategy_registry.register(
cls.strategy_name,
cls,
description=f"{cls.__class__.__name__}",
)
